One method of problem solving or decision-making is a process called “chunking”. Chunking is grouping or categorizing related issues or information into the smallest, most meaningful unit. Think about how hard it would be to read a 300-page book without sentences, paragraphs, and chapters. Chunking is a naturally occurring process that can be actively used to break down problems and communicate more efficiently.
Even though there is the possibility of losing sight of the big picture while using chunking, chunking allows logical grouping of data for easier understanding. Non-chunked data is harder to remember and many studies support the use of chunking as a memory tool ultimately helping the decision-making process.
How Chunking Works
Chunking works by pre-compiling bits of perceptual data. These chunks can then be recollected and combined into a picture that the mind can use to represent the situation. These chunks are then available for rapid recall. Experts in any field have more of these chunks available for a particular problem domain, which is also called experience. In his thesis, Mark Orr explains that the difference between experts and non-experts is “that due to the nature of their chunks. Expert chunks contain more information than novices chunks” (Orr, 2003, p. 3). This suggests that increasing the information available in each chunk helps perceive the keys to the problem at hand and leads to better decision-making.
The brain can formulate these chunks much better if the data are logically grouped. An important point to note is this chunking process occurs naturally but can be further refined. For instance, a marketing team may have a decision to make regarding a new product. Several ideas are mentioned including advertising, mail, free publicity, and even strategic partnerships. Even these broad chunks may be to big to deal with so the next logical step would be to break these big chunks down into smaller chunks. Advertising could be analyzed as print versus television or radio. Mail could include direct mail, email and coupons, and free publicity may include press releases and soft news. This breaks down the related groups into manageable areas to focus on while still allowing the bigger chunks to be used for summary data.
Chunking as a Motivational Tool
Many studies support the use of chunking as a motivational or learning tool. For example, “among students at community colleges who begin an Associate’s degree program, fewer than half have earned their degree or are still enrolled after five years” (Dins, 2005, p. 1). As a consequence, many community colleges are chunking the curriculum so that students have measurable milestones and are not so overwhelmed with the perceived amount of time and effort needed to finish. Dins writes in her doctoral dissertation about chunking in community college curriculum, “The purpose or possible advantage of chunking is that it will improve the rate of degree completion among community college students by allowing students to complete a degree non-sequentially and noncontinually, leading to better wages and career advancement” (Dins, 2005, p. 16).
Another study on stroke patients done in Hong Kong by Karen Pui Yee Liu, shows that chunking, combined with self regulation and mental rehearsal, “is useful for enhancing the relearning of trained and untrained tasks” (Lee, 2002, p. vi). If chunking can help with relearning, it would appear to also be useful for learning and decision-making.
When Chunking is not Right
Mark Orr, in his dissertation, reports that there is at least one decision making domain where chunking may not be the primary factor involved. He suggests, “in fact, some domains of expertise require dimensional sensitization mechanisms in addition to chunking mechanisms because of the dimensional nature of the domain environment (e.g. race car drivers and radiologists)” (Orr, 2003, p. xx).
Why use Chunking
The most important reason to use chunking is for easier understanding; non-chunked data is harder to remember. The human brain can hold between 7 – 12 chunks of information at a time. Think how much easier memorizing a social security number is versus a checking account routing number. Both of these are 9 digit numbers, but the social security number is broken up into 3 smaller chunks. The same principal applies to credit card numbers, addresses and phone numbers.
Improving the ability to structure data into more organized chunks enhances the ability to think. There is less time spent on reviewing the data because something was missed or misunderstood. Improving memory constraints increases the ability to synthesize fresh ideas, understand the problem more readily, and view the problem at hand.
A Practical Use of Chunking
In a recent analysis of absenteeism at a major Las Vegas hotel, the concept of chunking was applied. Absenteeism is complex problem with many possible causes. The participants first brainstormed possible ideas for absenteeism (which were themselves chunks). The data was then consolidated into logical categories or chunks. By chunking the ideas, data gathering would become easier to manage. Another side effect of chunking the data into categories made assigning participants to specific research tasks easier.
Even though much of this article discussed memory and learning techniques, understanding that the results of decision-making can be greatly improved by using chunking is very important. The easier these data chunks are categorized, the more these chunks are available to rely on in a particular problem domain. Experienced musicians can follow another musician who is improvising much easier than a novice. Experienced captains can predict tides, currents and ocean conditions better than a novice deckhand. This is all due to the brains ability to synthesize stimuli into chunks and process these chunks into long-term memory for later recall in the decision-making process.
In conclusion, chunking allows logical grouping of data for easier understanding for two main reasons. First, many studies support the use of chunking as a memory tool. But most important, non-chunked data is harder to remember.
Dins, K. (2005). “Chunking” Professional-technical programs to create pathways to degree completion in community colleges. (Doctoral dissertation, Oregon State University,2005). Retrieved May 14, 2007 from ProQuest Information and Learning
Lee, K. (2002). Mental imagery and learning for patients suffered from stroke. (Doctoral dissertation, Hong Kong,2002). Retrieved May 14, 2007 from ProQuest Information and Learning
Orr, M. (2003). Interactions between chunking and perceptual learning in expertise. (Doctoral dissertation, University of Illinois at Chicago,2003). Retrieved May 14, 2007 from ProQuest Information and Learning